Low dose X-ray image sequences, as obtained in fluoroscopy, exhibit high levels of noise that must be suppressed in real-time, while preserving diagnostic structures. Multi-step adaptive filtering approaches, often involving spatio-temporal filters, are typically used to achieve this goal.
Fluoroscopy involves exposing the subject to X-rays and capturing a sequence of images, to aid the physician during surgery or diagnosis. The advantages of real-time imaging and the freedom to freely position the X-ray field during examination makes fluoroscopy a very powerful diagnostic tool. However, due to the length of the fluoroscopic examinations, the exposure rate must be kept much lower than in common radiography, resulting in images that suffer from higher levels of quantum noise than ordinary radiographs. Also typical medical devices like catheters and guide-wires tend to have low contrast and are often difficult to distinguish from the background. These noisy images are then “cleaned” up using sophisticated filtering algorithms which would suppress the noise, but preserve and enhance the features of interest.
Barium studies to investigate the functioning of the gastrointestinal tract, orthopedic surgery for fracture correction and placement of metalwork, studies to detect problems in joint functioning, and angiography and catheter insertion are some of the applications of fluoroscopy. The length of the exams, the exposure rate, the diagnostic features of interest and the image enhancement algorithms, all vary widely across the applications.
The broad classes of algorithms used in these systems include contrast enhancement and spatial and temporal processing algorithms. Typically the spatial filters used in fluoroscopy need to be edge-preserving, while the temporal filters need to ensure that motion does not cause blurring or trailing artifacts. Spatio-temporal filtering attempts to combine the best of both approaches by using spatially filtered outputs if the pixel is affected by motion and temporally filtered outputs if the pixel is static. Some of these schemes may use additional steps to locate and enhance low-contrast objects of interest. The strength of these filters may be adapted using local statistics, object boundaries and motion estimates. They may try to detect application-specific structures, like catheters for angiography procedures. These algorithms often use images with sizes up to 1024×1024 pixels and support frame rates up to 30 frames per sec—thereby creating a need for high performance computational platforms. Spatio-temporal filtering approaches used in fluoroscopy are described in more detail in Richard Aufrichtig and D. L. Wilson, “X-Ray Fluoroscopy Spatio-Temporal Filtering with Object Detection”, IEEE Transactions on Medical Imaging, Vol. 14, No. 4, December 1995, pp. 773-746, which is incorporated by reference herein, and Gert Schoonenberg et. al, “Adaptive spatial-temporal filtering applied to x-ray fluoroscopy angiography”, Proceedings of SPIE Vol. 5744, Medical Imaging 2005, for example.
Other features of the present embodiments will be apparent from the accompanying drawings and from the detailed description that follows.